Determination of the Number of Clusters in a Data Set: A Stopping Rule × Clustering Algorithm Comparison

The accuracy of “stopping rules” for determining the number of clusters in a data set is examined as a function of the underlying clustering algorithm being used. Using a Monte Carlo study, various stopping rules, used in conjunction with six clustering algorithms, are compared to determine which ru...

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Veröffentlicht in:International journal of strategic decision sciences Jg. 2; H. 4; S. 1 - 13
1. Verfasser: Boone, Derrick S
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hershey IGI Global 01.10.2011
Schlagworte:
Algorithms
Analysis
Clustering
Datasets
Outliers (statistics)
ISSN:1947-8569, 1947-8577
Online-Zugang:Volltext
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Zusammenfassung:The accuracy of “stopping rules” for determining the number of clusters in a data set is examined as a function of the underlying clustering algorithm being used. Using a Monte Carlo study, various stopping rules, used in conjunction with six clustering algorithms, are compared to determine which rule/algorithm combinations best recover the true number of clusters. The rules and algorithms are tested using disparately sized, artificially generated data sets that contained multiple numbers and levels of clusters, variables, noise, outliers, and elongated and unequally sized clusters. The results indicate that stopping rule accuracy depends on the underlying clustering algorithm being used. The cubic clustering criterion (CCC), when used in conjunction with mixture models or Ward’s method, recovers the true number of clusters more accurately than other rules and algorithms. However, the CCC was more likely than other stopping rules to report more clusters than are actually present. Implications are discussed.
Bibliographie:ObjectType-Article-1
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ISSN:1947-8569
1947-8577
DOI:10.4018/jsds.2011100101